Artificial intelligence (AI) and machine learning (ML) are not the future of business — they are the now.
Already, it is estimated a fifth of UK businesses were using AI by the end of 2018, and nine in 10 are expected to have made some investment by the end of 2020.
Forbes puts the number in the US already investing in the technology at 47%, and there’s been significant investment in Europe in machine learning and AI capabilities as the European Commission prepares for the digital single market.
But while the numbers sound impressive, they are also not even, according to Baker Tilly.
A lot of the growth has been among the biggest companies — and the smallest.
For the midmarket, without the agility of start-ups or the top-end research and development investment dollars of very large enterprise, there is an urgent need to investigate how best to implement AI in their organisations.
Baker Tilly Chief Information Officer Adam Grainger says there are two things midmarket businesses can’t afford to be when it comes to machine learning and artificial intelligence: afraid and left behind.
Grainger said he subscribed to the theory that machine learning and AI technology would provide the impetus for more innovative human work practices rather than replacing people.
“It was suggested computers would replace workers decades ago and, more recently, Excel was the end of accounting as we knew it,” Grainger explained.
“But overall the effect has instead been that more opportunities and value have been created. Just think about the creation of the iPhone and more importantly, the App Store, and all the new jobs and industries that have been spawned by that.”
That was true not just in the businesses that Baker Tilly saw each day around the world, but also in the professional services firms that make up the global network, Grainger said.
“The key in the professional services environment will still be an understanding of our clients’ businesses, knowing when to act and having the trust of those clients.
“AI will help in each of these regards, but the human element is still needed.”
Grainger said midmarket companies also jeopardised their chances of success through being intimidated by the sheer size of investment in AI and machine learning being made by their bigger rivals.
The OECD has studied global AI investment and has found three key trends have emerged in the way private equity is investing in new technology providers in this space.
Firstly, Chinese start-ups in AI are the recipients of very big investments, about 10 times that of start-ups in other countries.
European start-ups are accelerating, picking up more investment, even if it is comparatively small, while the US is also seeing a surge in numbers as well as an increase in the size of investment — about double the size of deals in Europe.
At the other end of the scale are the big enterprise investments in AI. Microsoft bought five AI companies in 2018 to bring into its portfolio, Ford Motor Company invested a cool US$1bn in Argo AI last year, while Chinese tech giant Baidu invested US$1.3bn in AI tech.
But big investments such as these still make up a sizeable proportion of the total invested in AI in 2018, meaning many middle sized firms are yet to fully commit.
Grainger believes there is definitely room for midmarkets to get involved – even if that means partnering with “smaller fish.”
“Very large companies and of course start-ups are spending a huge amount of money in this area,” he said.
“However, this doesn’t mean more established, midmarket firms cannot investigate and fully utilise machine learning and AI.
“A large number of start-ups are actually created to service machine learning and AI solutions, specifically for midmarket firms that do not have the dollars to do everything in-house like the very largest firms.
“This can create opportunities for the midmarket to move faster than their bigger competitors, as the start-ups they work with can move quicker and in some cases, also tailor solutions for midmarket companies.
“The key for such companies is allocating time and budget to teams to focus on specific areas of their business where AI may be applied – whether that’s internal processes or external, client-facing work.
“Mid-market firms should focus on ‘starting small’ and create momentum in applying machine learning and AI in their organisations. “
Grainger feels the professional services sector is particularly conducive to technological innovation and that clients are already demanding it.
“All firms in the sector will need to leverage machine learning and AI in order to remain efficient at the very least and, in the case of traditional service lines like Audit, potentially to increase or maintain high-quality,” he said.
“Clients are also expecting machine learning and artificial intelligence to be used in the provision of standard services and also from an advisory point of view, helping the client to understand what machine learning and AI are and how their business can leverage them.
“Machine learning and AI will also create new assurance and advisory opportunities as clients and investors alike seek evidence of their impact.
“While robotics are definitely having the biggest impact right now, machine learning and AI are already huge marketing tools – clients are actively asking what tools our firms are using.”
Grainger said professional services firms faced a number of key questions around the ongoing growth of artificial intelligence and machine learning – notably about privacy, staff recruitment and training and richer rivals competing for the same technological advancements, experts and graduates.
But he predicted Baker Tilly members would be particularly suited to converting these challenges into opportunities.
“Our network of firms is well-placed to develop technologies and advise clients given the network’s geographical spread and presence across the globe, which provides access to a huge range of machine learning and AI providers and experts,” he explained.
“The network, in effect, is more than 125 different start-ups, with a wealth of knowledge and ideas that they can share with one another.
“Ultimately, we still have the skills and expertise to advise the middle market, that others do not. Machine learning and artificial intelligence will aid our already excellent advisors in this space, not replace them.
“In the same vein, such solutions cannot make a mid-market advisor out of anyone, even if they have general expertise in the sector overall – experience and relative information/advice are key.
“The network’s firms are able to come together with a range of different ideas and experiences, to work together to select, develop, implement and continually update the right solutions for our clientele, the middle market.”
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DISCLAIMER: All opinions, conclusions, or recommendations in this article are reasonably held by Baker Tilly at the time of compilation but are subject to change without notice to you. Whilst every effort has been made to ensure the accuracy of the contents in this article, the information in this article is not designed to address any particular circumstance, individual or entity. Users should not act upon it without seeking professional advice relevant to the particular situation. We will not accept liability for any loss or damage suffered by any person directly or indirectly through reliance upon the information contained in this article.
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